“Biometrics” is a term of art that refers to automated methods for identifying people or verifying a person's identity based on their unique physiological characteristics or behavioral traits. Various types of biometric identification or verification methods include fingerprint scanning, iris scanning, retina scanning, facial characteristics analysis, handwriting analysis, handprint recognition and voice scanning or voice print analysis. The word “biometrics” may also be used to refer to the field relating to biometric identification. The use of “biometric” as an adjective pertains to technologies that utilize behavioral or physiological characteristics to determine or verify identity. “Biometric sample” may refer to an identifiable, unprocessed image or recording of a physiological or behavioral characteristic, acquired during submission, used to generate biometric templates. As used herein a “template” is a mathematical representation of a biometric sample. Templates can vary in size, for example from 9 bytes for hand geometry to several thousand bytes for a facial recognition file. The phrase “biometric data” as used herein, refers to a processed sample in a form that is capable of being stored electronically or similarly archived as a template. A “biometric system” as used herein is integrated biometric hardware and software used to conduct biometric identification or verification. “Enrollment” as used herein is the initial process of collecting one or more biometric samples from a subject and storing the resulting biometric data as a template for later comparison. “Feature extraction” is an automated process of locating and encoding distinctive characteristics from a biometric sample in order to generate a template.
In the biometric arts “authentication”, generally speaking is a process for establishing the validity of a user attempting to gain access to something such as a system, service or location. The terms “identification” and “recognition” are synonymous in the biometric arts. In both processes, a sample is presented to a biometric system during enrollment. The system then attempts to find out who the sample belongs to, by comparing the sample with a database of samples in an attempt to find a match. This is also known as a one-to-many (1:n) comparison. In contrast, “verification” is a one-to-one comparison in which the biometric system attempts to verify an individual's identity. Verification (1:1 matching) is the process of establishing the validity of a claimed identity by comparing a verification template to an enrollment template. Verification typically requires that an identity be claimed by a subject, after which an enrollment template of the subject is located and compared with a verification template derived from a sample provided by the subject. If the two samples match, the biometric system confirms that the subject is who he or she claims to be. Typically the subject of verification is using a facility or service of a company, institution or the like. Some verification systems perform very limited searches against multiple enrollee records. For example, a user with three enrolled finger-scan templates may be able to use any one of the three fingers to verify his identity. One-to-few (1:few) matching is a middle ground between identification and verification. This type of authentication involves identification of a user from a very small database of enrollees. While there is no exact number that differentiates a 1:n from a 1:few system, any system involving a search of more than 500 records is likely to be classified as 1:n. A typical use of a 1:few system may be to control access to sensitive rooms at a 50-employee company or the like, where users place their finger on a device and are identified from a small database. Thus, identification and recognition involve matching a sample against a database of many, whereas verification involves matching a sample against a database of one. A key distinction between these two approaches centers on the questions asked by the biometric system and how this fits within a given application. During identification, the biometric system asks, “Who is this?” and establishes whether a biometric record exists, and, if so, provides the identity of the enrollee whose sample was matched. During verification, the biometric system asks, “Is this person he or she claims to be?” and attempts to verify the identity of a subject. Herein, the word “authentication” will be used broadly to generally encompass identification, recognition and verification.
The biometrics industry is growing relatively slowly. One factor contributing to this slow growth is convenience of use due to availability of equipment and related systems. For example, in law enforcement hardware needs to be stable and durable, however biometric equipment with such stability and durability is not widely available. Nevertheless, utilizing biometrics for personal authentication is convenient for end users and considerably more accurate than previously used methods such as the utilization of passwords or Personal Identification Numbers (PINs). The use of biometrics is convenient for the end user as there is nothing for the subject to carry or remember. The aforementioned accuracy is due in large part to biometrics linking an event to a particular individual, whereas a password or token may be used by someone other than the authorized user. Biometric authentication is based on an identification of an intrinsic part of a human being. Tokens, such as smart cards, magnetic stripe cards, physical keys, and so forth, can be lost, stolen, or duplicated. Passwords can be forgotten, shared, or observed. Furthermore, electronic biometric techniques can provide an audit trail. Biometrics are becoming widely accepted, familiar and inexpensive. Currently, fingerprint and voice authentication are popular biometrics. Voice printing is commonly used in conjunction with a PIN or similar identification number, although accuracy is always questionable depending on how and where this biometric is used.
Typically, a good biometric system is low cost, fast, accurate and easy to use. There are certain common characteristics that make Biometric systems for identification and/or authorization more acceptable. Typically, the biometric must be based upon a distinguishable trait. For example, for nearly a century, law enforcement has used fingerprints to identify people. There is a great deal of scientific data supporting the concept that no two fingerprints are alike. However, newer methods, even those with a great deal of scientific support, such as DNA-based genetic matching, are typically more slowly adopted or accepted. Individuals may find biometric authentication relatively acceptable in that most people find it acceptable to have their pictures taken by video cameras or to speak into a microphone. Typically, at least in the United States, using a fingerprint sensor does not seem to be objectionable to most people.
Cost considerations for a biometric identification or verification system not only include the initial cost of the sensor and matching software that is involved in a biometric Solution, but often, life-cycle support cost of providing system administration support. Additional biometric identification and verification system operation costs may include employment of an enrollment operator or similar personnel. Thus, operation costs for a biometric identification and verification system can quickly overtake the initial cost of the hardware and software.
Terms of art that are used to describe the accuracy of biometric systems include false-acceptance rate, false-rejection rate, and crossover-error or equal-error rate. False-acceptance rate (FAR) may be viewed as the percentage of imposters incorrectly matched to a valid user's biometric. False-rejection rate (FRR) may be viewed as the percentage of incorrectly rejected valid users. The crossover error rate (CER) is the error rate at which FAR equals FRR and is used as a comparison metric for different biometric devices and technologies. The lower the CER, the more accurate and reliable the biometric device. In biometrics, it is typically beneficial to consider the crossover-error rate (or at least the false-acceptance rate) and the false-rejection rate together. For many biometrics systems, an acceptance threshold can be set, typically by a biometric system administrator, to establish the degree of correlation necessary for a comparison to be deemed a match. This threshold may be set to ensure that virtually no impostors will be accepted. Problematically, such a restrictive threshold results in at high false-rejection rate, and thus an unreasonably high number of authorized users will be rejected. Using biometrics in any industry there is always the possibility of a false positive, depending on how it is used. For example, an iris scan might be affected by an eye condition or an individual may not have a particular finger or hand required for a fingerprint scan or a hand scan. Such false positive are unacceptable in many circumstances, including many circumstances occurring in an incarceration or law enforcement environment which might result in detention or release of the wrong individual.
Biometric technologies are becoming a foundation of an extensive array of highly secure identification and personal verification solutions and are coming into use not only in private industry, but also by federal, state, and local governments, including law enforcement agencies. Law enforcement has already widely adopted fingerprinting and fingerprint scanning. However, there is an increasing need for automated authentication technologies in the law enforcement community, particularly solutions well suited for use in law enforcement and incarceration environments. Such systems need to provide certainty of identification in certain circumstances while convenience of use is a primary concern in other circumstances.